Aiming at the problem of small object miss detection in object detection process， an improved YOLOv5 （You Only Look Once） object detection algorithm based on attention mechanism and multi-scale context information was proposed. Firstly， Multiscale Dilated Separable Convolutional Module （MDSCM） was added to the feature extraction structure to extract multi-scale feature information， increasing the receptive field while avoiding the loss of small object information. Secondly， the attention mechanism was added to the backbone network， and the location awareness information was embedded in the channel information， so as to further enhance the feature expression ability of the algorithm. Finally， Soft-NMS （Soft-Non-Maximum Suppression） was used instead of the NMS （Non-Maximum Suppression） used by YOLOv5 to reduce the missed detection rate of the algorithm. Experimental results show that the improved algorithm achieves detection precisions of 82.80%， 71.74% and 77.11% respectively on PASCAL VOC dataset， DOTA aerial image dataset and DIOR optical remote sensing dataset， which are 3.70， 1.49 and 2.48 percentage points higer than those of YOLOv5， and it has better detection effect on small objects. Therefore， the improved YOLOv5 can be better applied to small object detection scenarios in practice.